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Atmospheric pollution became a big issue in densified urban areas where the ventilation in streets is not sufficient. It is particularly the case for street surrounded by high buildings so-called street canyons. The ventilation and, thus, the concentrations in this kind of street are highly relying on geometric properties of the street (width of the street, heights of the buildings, etc.). Reynolds-averaged Navier-Stokes equations are used to investigate the impact of two geometric street ratios on pollutant dispersion: the ratio of the leeward to the windward building height (H1/H2) and the ratio of the street width to the windward building height (W/H2). The aim is to quantitatively assess the evolution of mean pollutant concentrations in the case of step-down street canyons with H1/H2 ranging from 1.0 to 2.0 and street width ratios W/H2 ranging from 0.6 to 1.4. Three types of recirculation regimes could be established, depending on the number and the direction of the vortices occurring inside and outside the canyon. Evolution of pollutant concentrations as a function of both ratios is provided as well as the recommended regimes in the perspective of reducing pollutant concentration in step-down street canyons at pedestrian level and near building faces.
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DOI : 10.1016/j.jweia.2019.104032
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CFD evaluation of mean pollutant concentration variations in step-down
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street canyons as a function of aspect ratio
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Nicolas Reiminger1,2*, José Vazquez2, Nadège Blond3, Matthieu Dufresne1, Jonathan Wertel1
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1AIR&D, 67400, Illkirch-Graffenstaden, France
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2ICUBE Laboratory, CNRS/University of Strasbourg, 67000, Strasbourg, France
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3LIVE Laboratory, CNRS/University of Strasbourg, 67000, Strasbourg, France
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*Corresponding author: Tel. +33 (0)3 69 06 49 40, Mail. nreiminger@air-d.fr
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Please cite this paper as : Reiminger, N., Vazquez, J., Blond, N., Dufresne, M., Wertel, J., 2020. CFD
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evaluation of mean pollutant concentration variations in step-down street canyons. Journal of Wind
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Engineering and Industrial Aerodynamics 196, 104032. DOI: 10.1016/j.jweia.2019.104032
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Abstract:
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Atmospheric pollution became a big issue in densified urban areas where the ventilation in
14
streets is not sufficient. It is particularly the case for street surrounded by high buildings so-
15
called street canyons. The ventilation and, thus, the concentrations in this kind of street are
16
highly relying on geometric properties of the street (width of the street, heights of the buildings,
17
etc.) A Reynolds-averaged Navier-Stokes model is used to investigate the impact of two
18
geometric street ratios on pollutant dispersion: the ratio of the leeward to the windward building
19
height (H1/H2) and the ratio of the street width to the windward building height (W/H2). The
20
aim is to quantitatively assess the evolution of mean pollutant concentrations in the case of step-
21
down street canyons with H1/H2 ranging from 1.0 to 2.0 and street width ratios W/H2 ranging
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from 0.6 to 1.4. Three types of recirculation regimes could be established, depending on the
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number and the direction of the vortices occurring inside and outside the canyon. Evolution of
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pollutant concentrations as a function of both ratios is provided as well as the recommended
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regimes in the perspective of reducing pollutant concentration in step-down street canyons at
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pedestrian level and near building faces.
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Keywords: Air quality, Computational fluid dynamics, Street Canyon, Aspect ratio, Building
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characteristics
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1. Introduction
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Air quality has become a major concern, especially in urban areas where air pollutant sources
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are numerous and population density is high. Air quality is influenced by traffic-related
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emissions and the local atmospheric environment which is highly dependent on street geometry.
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Indeed, narrow streets surrounded by high buildings are more often subject to high pollutant
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concentrations than wide streets with lower building heights, due to poorer ventilation. An
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estimation of pollutant concentrations in streets depending on building configurations could
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help urban planners to understand the impacts of street geometry on air quality and provide
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keys to making suitable choices to lessening air pollution levels, as one of the key point
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discussed by Bibri and Krogstie (2017) in order to achieve smart sustainable cities of the future.
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The effects of street geometry on pollutant dispersion have already been studied extensively
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with both experimental (Gerdes and Olivari, 1999; Hotchkiss and Harlow, 1973; Pavageau and
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Schatzmann, 1999; Vardoulakis et al., 2003) and numerical methods (Aristodemou et al., 2018;
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Bijad et al., 2016; Santiago and Martin, 2005; Tominaga and Stathopoulos, 2017; Vardoulakis
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et al., 2003) and also at full-scale with in situ measurements (Qin and Kot, 1993; Vardoulakis
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et al., 2002). Some authors have even studied the effects of roof shape on pollutant dispersion
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(Takano and Moonen, 2013; Wen and Malki-Epshtein, 2018). However, most of these works
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were conducted in symmetrical street canyons using buildings with the same height. Indeed,
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streets surrounded by buildings of the same height do exist although streets with different
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building heights, so-called asymmetrical street canyons, are found more often. Addepalli and
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Pardyjak (2015) studied cases of step-down street canyons with a taller building on the leeward
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side and showed that there are significant modifications of flow patterns depending on building
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height and street width ratios. Xiaomin et al. (2006) performed a similar work with different
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kinds of streets, including deep and wide symmetrical streets and step-up and step-down
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asymmetrical streets, and showed that there are three major types of regimes in street canyons
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depending on height and width ratios, especially in the case of step-down street canyons. In
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spite of the several studies already done, and although there is a need for urban planners and
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decision makers, quantitative information on how concentrations evolve with the modification
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of street geometry is still lacking. Thus, further work is required in this direction.
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The aim of this work is to provide information on how mean pollutant concentrations evolve
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quantitatively in the case of step-down street canyons according to two specific ratios: the ratio
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of the leeward building height to the windward building height H1/H2 and the ratio of the street
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width to the windward building height W/H2, determined by computational fluid dynamics
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(CFD) simulations. Section 2 presents the numerical model used in this work with the governing
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equations, the boundary conditions and the numerical settings. Section 3 presents the validation
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of the model versus experimental data in which a mesh sensitivity test and an evaluation of the
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best turbulent Schmidt number are carried out. Finally, section 4 describes the results of the
66
study for several mean concentrations and a discussion of the results is proposed in section 5.
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2. Numerical model
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2.1. Computational domain and boundary conditions
70
Fig. 1 shows the computational domain of the street canyon, the dimensions of interest, the
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localization of the different boundary conditions and the domain size.
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In this study, H1 corresponds to the height of the leeward building, H2 corresponds to the height
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of the windward building, W corresponds to the width between the two buildings and L
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corresponds to the length of the street. Here, we study the case of a long canyon (L/W>5) with
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the assumption that the interactions in the y-direction are negligible. To ensure this assumption
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a 3D simulation was computed for this study, and the results were compared to 2D results.
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Using a street canyon with L/W=10, it was found that the differences between 2D and 3D
78
simulation are fewer than 8% for |y|≤3H with y=0H the center plane of the street. For
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3H<L/W<5H, differences are still acceptable but can reach 20%. According to this results, all
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simulations were done in 2D in order to reduce calculation costs.
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We followed the recommendations given by Franke et al. (2007) concerning the boundary
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conditions and the domain size: the inlet boundary is placed 7×H2 away from the canyon; a
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symmetry condition is applied at the top and the lateral boundaries, with the top placed 6×H2
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away from the roofs of the buildings; the outlet boundary is placed 15×H2 away from the street
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to allow for flow development using a freestream outlet, and no-slip conditions were applied to
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all the other boundaries (roofs/walls of the buildings and the ground).
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Fig. 1. Sketch of the computational domain
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2.2. Governing equations
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CFD simulations were carried out in OpenFOAM 5.0. Since in real contexts, full steady state is
91
not always reached, all the simulations were performed using the unsteady pimpleFoam solver
92
which is able to capture time instabilities. Reynolds-averaged Navier-Stokes (RANS)
93
methodology was used to solve the continuity and the momentum equations throughout the
94
computational domain by considering air as an incompressible fluid. This assumption can be
95
made because of the low wind velocities (<10m/s) which give low Mach numbers. The
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corresponding continuity (1) and momentum (2) equations are given below:
97

  (1)
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
 
 



 (2)
99
where and
are the ith mean and the fluctuating velocities, respectively, is the ith
100
Cartesian coordinate,
is the mean pressure and is the kinematic viscosity.
101
Using RANS to solve turbulent flows requires choosing a turbulence model to solve the
102
Reynolds stress tensor
(3). The RNG k-ε model proposed by Yakhot et al. (1992) was
103
chosen for turbulent closure because the numerical results fitted well with the experimental data
104
(see section 3.1.). The corresponding equations for turbulent kinetic energy (4) and turbulent
105
dissipation rate (5) of the RNG model are given below. Taking R=0 and using the correct
106
constants, these equations also correspond to the standard k-ε model.
107
 

 (3)
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
 






 (4)
109

 







 (5)
110
 

(6)
111
 
(7)
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where    and   the mean strain tensor, is the ith mean velocity, is the
113
ith Cartesian coordinate, is the kinematic viscosity, k is the turbulent kinetic energy, is the
114
turbulent dissipation rate,  is the Kronecker delta and is the turbulent viscosity. All the
115
other parameters are model constants given in Table 1 for both the standard and the RNG k-ε
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models.
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Table 1. Turbulence model constant values
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Model
Cµ
Cε1
Cε2
σk
σε
η0
β
Standard k-ε
0.09
1.45
1.9
1.0
1.3
-
-
RNG k-ε
0.085
1.42
1.68
0.72
0.72
4.38
0.015
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Pollutants are considered as passive scalars since no chemical effects are solved in this study.
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The equation governing advection-diffusion for the passive pollutant dispersion given in
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OpenFOAM was modified to take into account turbulent diffusivity. The corresponding
122
equation is given below:
123

 



 (7)
124
where C is the pollutant concentration, is the molecular diffusion coefficient,  is the
125
turbulent Schmidt number and is the source term of the pollutants (emissions).
126
The ratio 
corresponds to the turbulent diffusion coefficient. The value of  is constant
127
throughout the computational domain and fixed at 0.2. This value was chosen for the validation
128
step (see section 3.2.).
129
2.3. Numerical settings
130
Second order schemes were adopted for all the gradient, divergent and Laplacian terms. In
131
particular, for the Laplacian terms we used the ‘Gauss linear corrected’-scheme which is an
132
unbounded second order conservative scheme, the second order ‘Gauss linear’-scheme for the
133
gradient terms and the ‘Gauss linearUpwind’-scheme for the divergent terms, the latter scheme
134
being an unbounded upwind second order scheme.
135
All the simulations were run until the convergence was reached. To ensure the convergence of
136
the simulations, the values of the streamwise velocity U and the pollutant concentration C were
137
monitored for several points all over the canyon. Since all the simulations reached steady-state,
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they were stopped when the values monitored were constant over time. Moreover, at the end of
139
the simulations all the residuals were under 10-5.
140
3. Model validation
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The model was validated versus the experimental wind tunnel data proposed by Soulhac et al.
142
(2001). This experiment setup consists of a regular street canyon with H1/H2=1 and W/H2=1
143
with a gas released continuously at the center of the street. A summary of the boundary
144
conditions used for this validation is given in Table 2. A comparison between experimental and
145
numerical streamwise velocity was made to evaluate mesh sensitivity; another comparison
146
between experimental and numerical pollutant concentrations was made to find the turbulent
147
Schmidt number which gave the best results compared to the experiment.
148
149
Table 2. Summary of the boundary conditions
150
Inlet
Experimental velocity profile which corresponds to a power law profile with
  
, where =5.54m/s is the velocity at , =0.63m is the reference height,
=0.127 is the power law exponent and z the height from the ground.
  , with    
the turbulent intensity, with   the Reynolds
number where U=4.43m/s is the mean inlet velocity, H=0.6m is the injection height and =1.56.10-
5 is the kinematic viscosity.
   
with =0.085 the CFD constant, and the turbulence length taken as equal to the
injection height (0.6m).
Outlet
Freestream outlet
Top
Symmetry plane
Lateral surfaces
Symmetry plane
Ground and
building surfaces
No slip condition (U=0m/s)
Emission
Line source with emission rate qm=1.10-4 µg/s localized at the middle of the street
151
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3.1. Mesh sensitivity
153
Mesh sensitivity tests were carried out and compared to the experimental streamwise velocity
154
results to find the best compromise between the precision of the numerical results and
155
calculation costs.
156
Fig. 2. shows this comparison for three localized velocity profiles: on the leeward side of the
157
street (x/H=-0.2), in the middle of the street (x/H=0.0) and on the windward side of the street
158
(x/H=0.2). Three mesh-dependent results are proposed and the grid expansion ratio between
159
the coarse and the medium grid and between the medium and the fine grid is 2. Velocities and
160
heights are proposed in dimensionless form, corresponding to U/Umax with Umax=5m/s and z/H
161
with H=0.1m, respectively.
162
The results show good agreement between the experimental and numerical data whatever the
163
mesh refinement considered. There is a noticeable difference in the numerical results between
164
the coarse and the medium mesh in the street canyon (z/H<1). The difference between the
165
medium and the fine meshes is almost imperceptible apart from the low heights for which the
166
fine mesh results are closer to the experimental results. Thus, in the light of these results, the
167
fine mesh grid was adopted.
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169
Fig. 2. Vertical distribution of numerical streamwise velocities for different mesh refinements compared to Soulhac et al.
170
(2001) experimental data
171
172
An additional mesh sensitivity study was performed on the variable of interest C, the pollutant
173
concentration, using the Grid Convergence Index (GCI) methodology proposed by Roach
174
(1994). This methodology is used to assess the mesh-related errors of a given mesh grid in view
175
of the fine and coarse grid results and depending on the grid expansion ratio and the order of
176
the numerical scheme used. The GCI for fine mesh grid error evaluation is given below:
177
  
 (8)
178
where and are the results using the fine and coarse grid, respectively (here   and
179
 ), r is the grid expansion ratio between the fine and the coarse grid and p is the
180
order of the numerical scheme.
181
The grid convergence index for the fine grid was calculated for 370 points uniformly distributed
182
in the street canyon with p = 2 (second order schemes) and r = 4 (the fine mesh is four times
183
   








   





   

  

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smaller than the coarse mesh). The corresponding mean  is 2% and the maximum
184
4%, thus corresponding to a sufficient grid resolution. The typical dimension of the chosen cells
185
is 0.0125 × H2.
186
3.2. Turbulent Schmidt number
187
According to Tominaga and Stathopoulos (2007), the optimal values of the turbulent Schmidt
188
number  are widely spread between 0.2 and 1.3 and have a considerable influence on
189
pollutant mass transfer. Thus,  must be chosen with care. To make this choice, several
190
simulations were performed for 0.1<<0.7 with steps of 0.1 and the results were compared
191
with the experimental data.
192
Fig. 3. shows the results for three localized concentration profiles: close to the leeward building
193
(x/H=-0.4), in the middle of the street (x/H=0.0) and close to the windward building (x/H=0.4).
194
The three closest numerical results compared to the experiment are shown and differ only by
195
the turbulent Schmidt number used: 0.1, 0.2 and 0.3. Concentrations and heights are proposed
196
in dimensionless form. The same dimensionless form as before was used for the heights (z/H)
197
and the dimensionless concentration was obtained using (9).
198
  (9)
199
where C* is the dimensionless concentration, C is the concentration, UH is the velocity just over
200
the windward building, H2 is the windward building height, L is the pollutant injection length
201
and qm is the pollutant emission rate.
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203
Fig. 3. Vertical distribution of numerical dimensionless concentrations for different Sct compared to Soulhac et al. (2001)
204
experimental data
205
206
The results show good agreement between the numerical and experimental data for =0.2.
207
Regarding this turbulent Schmidt number, for the leeward side there is generally an
208
overestimation of the concentrations in the upper part of the street and an underestimation in
209
the lower part of street while there is a general underestimation for the windward side. The
210
numerical results are less accurate with =0.1 and =0.3, so the value of 0.2 was kept for
211
the rest of the study. Using this turbulent Schmidt number, the mean normalized absolute error
212
over the experimental profiles was 10%. The corresponding 95th percentile was less than 30%
213
and the maximal differences between the experimental and numerical results occurred near the
214
ground.
215
The models used in the present paper (RANS and RNG k-ε) give a global underestimation of
216
the turbulent momentum diffusion leading to low turbulent Sct. The turbulent Schmidt number
217
taken as 0.2 is in coherence with other authors results who took a low Sct as 0.3 for the same
218
models (Tominaga and Stathopoulos, 2007). It should be noted that the value of 0.2 could not
219
be the best for all the geometric ratios considered in this work. However, it was decided to
220
always use the same Sct in the whole study, which is a common practice done by the scientific
221
   





   





   

  

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community (Takano and Moonen, 2013 ; Wen and Malki-Epshtein, 2018 ; Cui et al., 2016), in
222
order to only compare the influence of the geometric properties of the buildings on the mean
223
concentrations and to avoid multi parameter comparisons.
224
4. Effects of street dimensions on mean concentrations
225
Exactly the same conditions as defined previously were used for the present study, except for
226
the geometric properties of the street and in particular H1 and W. To study the mean
227
concentrations in the street canyon, several couples of height ratios H1/H2 and width ratios
228
W/H2 were considered. The present work is limited to a step-down street canyon configuration
229
where H1/H2>1.0. The following height ratios were used: 1.0, 1.2, 1.4, 1.6, 1.8 and 2.0. For
230
each of these height ratios, 5 width ratios were considered: 0.6, 0.8, 1.0, 1.2 and 1.4, giving a
231
total number of 30 simulations and an overall idea of how could evolve mean concentrations in
232
step-down street canyons. This number does not include certain particular cases that were also
233
simulated when the results were strongly different between two cases (e.g. when for a given
234
width ratio, two successive height ratios results in two different regimes). A case table of all the
235
ratios considered in this work is proposed in Table 3.
236
Table 3. Case table of all geometric ratios considered ( : couples of ratios initially considered, : specific cases considered
237
aftermath)
238
W/H2
0.6
0.8
1.0
1.2
1.4
H1/H2
2.0
1.9
1.8
1.7
1.6
1.5
1.4
1.3
1.2
1.1
1.0
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Fig. 4 shows the localization of the mean concentrations studied in this paper. Here, we study:
240
- The concentration averaged all over the street (in the W×H2 area),
241
- The mean concentration on a vertical profile placed 0.1H2 from the windward building
242
facade (concentration averaged for the H2 height) and another vertical profile placed 0.1H2
243
from the leeward building facade (concentration averaged for the H2 height). These mean
244
concentrations are relevant for people living in the buildings near the street.
245
- The mean concentration for a horizontal profile placed 0.1H2 from the ground
246
(concentration averaged for the W length). This mean concentration is relevant for
247
pedestrians in the street.
248
249
250
Fig. 4. Localization of the mean concentrations studied.
251
252
All the concentrations will be given in dimensionless form. The dimensioned concentrations
253
could also be retrieved using (9) with =2.75m/s, H2=0.1m, L=0.0025m and =1.10-4 µg/s.
254
4.1.Vorticity and recirculation regimes in the street canyon
255
Flow velocities and recirculation patterns have a significant impact on pollutant dispersion and
256
thus on pollutant concentrations inside and outside the street canyon. The modifications of flow
257
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velocities and recirculation patterns are caused solely by the geometric properties of the street
258
(H1/H2 and W/H2) as all the simulations were run using the same velocity inlet profile.
259
Out of the total number of simulations performed, three types of recirculation regimes were
260
found. Fig. 5. shows an example of each regime with the velocity vectors and the corresponding
261
y-vorticity given by equation (10). These three regimes stand out due to their number of
262
recirculation zones inside and outside the canyon.
263

 
 (10)
264
Regime A corresponds to a big single vortex localized in the canyon. For this regime, vorticity
265
is globally positive in the canyon, which means that the vortex rotates clockwise. Regime B
266
corresponds to two vortices, one large vortex in the canyon and a second localized mostly over
267
the canyon and the windward building. The large vortex in the canyon is very similar to that of
268
regime A, but here the vorticity is mostly negative, and the vortex rotates counterclockwise.
269
The second vortex localized outside the canyon rotates clockwise. Regime C corresponds to
270
three vortices, two contra-rotative vortices localized in the canyon and the third vortex mostly
271
localized over the windward building. This regime appears to be a combination of regimes A
272
and B, with the clockwise-vortex of regime A in the low part of the street and the
273
counterclockwise-vortex of regime B situated just over it. The same clockwise-outside-vortex
274
of regime B is also observed.
275
Xiaomin et al. (2006) gave the critical value of H1/H2 for several W/H2 corresponding to the
276
limit between regime A and regime B/C without distinction between B and C. Their results are
277
compared with those of the present study for W/H2 from 0.6 to 1.4 and are shown in Fig. 6. with
278
the gray area corresponding to the switching area between regime A and regime B/C. The
279
boundary conditions were the same between both studies.
280
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282
Fig. 5. Recirculation patterns, velocity vectors and y-vorticity for different geometric ratios H1/H2 and W/H2
283
284
The results obtained after the simulations showed a trend similar to that of the results of
285
Xiaomin et al. (2006). The critical value of H1/H2 increases when the distance between the
286
buildings increases and the zone of change between regime A and regime B/C is quite similar
287
for both studies. However, critical values seem to be reached sooner according to our results
288
(i.e. for smaller H1/H2) with a maximal difference of 0.1 compared to the results of Xiaomin et
289
al.
290
Some simulations were rerun using the turbulent conditions of Xiaomin’s et al., that is, using
291
the standard k-ε turbulent closure. The results, also presented in Fig. 6., show this time perfect
292
concordance between both studies. Thus, turbulent closure schemes have an influence on the
293
critical values of H1/H2. This difference between critical values when using standard k-ε or
294
RNG k-ε are, however, quite small with a maximum difference of 0.1 for the ratio H1/H2.
295
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296
Fig. 6. Comparison of regime changing zones between the present study and the results of Xiaomin et al. (2006) using RNG
297
and standard k-ε turbulent closure.
298
299
4.2. Impact of the regimes on pollutant dispersion
300
Three examples of pollutant dispersion in the street canyon for each regime are shown in Fig. 7.
301
The overall concentrations in the street canyons being very different between the three regimes,
302
the color scale is different for each of them. The velocity vectors are provided in order to better
303
understand the differences in the concentration fields for the three regimes.
304
The evolution of the concentration field, the overall magnitude of concentration, and the most
305
impacted building are directly linked with the type of regime being established. In regime A,
306
the pollutants released at ground level are mostly dispersed towards the leeward building due
307
to the single clockwise vortex established in the street. In regime B, the apparition of a second
308
vortex due to the increase of the leeward building height and the decrease of the distance
309
between building leads to a change in the dispersion of pollutants. The vortex in the street being
310
in this case counter clockwise, the most impacted building became the windward building.
311
Moreover, concentrations are overall higher in this case and it seems to be the consequence of
312








   








 







DOI : 10.1016/j.jweia.2019.104032
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the clockwise vortex localized just above which is driving a part of the pollutants which left the
313
street to the street again. For the last regime, regime C, both buildings are highly impacted. The
314
difference with the regime B is not only the apparition of a third vortex, but the fact that two
315
vortices are localized in the street between the buildings. Due to this two vortices, the pollutants
316
released at ground level are dispersed to the leeward building but, because of the second vortex
317
in the canyon, they are more homogenized in the low part of the street and seem to be more
318
stagnant. It should also be noted that global velocities in the street tend to decrease with the
319
increase of the leeward building height and the decrease of the distance between building which
320
also conduct to higher pollutant concentrations.
321
322
Fig. 7. Three examples of dimensionless concentrations in a street canyon for each type of regime.
323
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4.3. Mean concentration in the street canyon
324
Initially, the results were studied by considering the mean concentrations of the whole street.
325
Fig. 8 shows the dimensionless street averaged concentrations (i.e. the mean concentration of
326
the H2 surface) proposed for several H1/H2 and W/H2 ratios and the different types of
327
regime are also specified.
328
329
330
Fig. 8. Dimensionless street averaged concentrations according to the ratio H1/H2 and W/H2
331
332
The results show that the evolution of mean concentrations is highly dependent on the type of
333
regime in place. The mean concentrations are indeed highest when regime C is in place and
334
lowest when regime A is in place.
335
In regime A, for a given distance between buildings (i.e. a given W/H2), the mean
336
concentrations are the same whatever the height of the leeward building. Thus, only the distance
337
between buildings has an impact on the mean concentrations in the street. For a fixed leeward
338
building height, the mean concentrations in the street increase when the distance between
339
buildings decrease. This increase is not constant and becomes higher when ratio W/H2
340
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decreases. For example, the mean concentration increases by 23% between W/H2=1.2 and
341
W/H2=1.0 and then by 37% between W/H2=1.0 and W/H2=0.8. Lastly, for the H1/H2 and
342
W/H2 ratios studied in this work, the factor between the lowest and the highest mean
343
concentration for regime A is equal to 2.
344
In regime B, the evolution of the mean street concentrations is dependent on both ratios H1/H2
345
and W/H2: for a given leeward building height, the mean street concentrations increase when
346
the distance between the buildings decreases; for a given distance between buildings, the mean
347
concentration increases when the leeward building height increases. In addition, the increases
348
between mean concentrations are not constant and become higher when H1/H2 increases and
349
W/H2 decreases. The factor between the highest and lowest mean concentrations in the case of
350
regime B is around 5.
351
In regime C, the evolution of the street mean concentrations is also dependent on both ratios
352
H1/H2 and W/H2 but is no longer monotonous. Indeed, for a given distance between the
353
buildings, the mean street concentrations first increase and then become constant. If the leeward
354
building height is high enough, this mean concentration can then decrease. In this third case, a
355
maximal mean concentration is reached. Mean street concentrations are highest for this regime
356
with, in the worst-case concentrations, 50 times that of the regular case H1/H2=W/H2=1.0.
357
Lastly, considering the whole series of simulations run in this study, for a given H1/H2 ratio,
358
the mean concentrations increase as the distance between buildings decreases, whatever the
359
three regimes observed. The evolution of the mean concentrations for a given W/H2 is
360
nevertheless dependent on the regime.
361
362
363
364
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4.4. Mean concentration on the building sides
365
The results were then studied considering only the windward and the leeward building sides.
366
Fig. 9 shows the dimensionless windward side averaged concentrations (i.e. the mean
367
concentrations averaged over the windward profile) proposed for several H1/H2, and W/H2
368
ratios and the different types of regime are also specified. Fig. 10 gives the same information,
369
but considering the dimensionless, averaged leeward side concentrations (i.e. the mean
370
concentrations averaged over the leeward profile).
371
As can be seen in Fig. 9 and Fig. 10, the evolution of the mean concentrations on the two
372
building sides are similar. However, the mean concentrations could be higher or lower on the
373
windward side, depending on the recirculation regimes.
374
In Regime A, for a given distance between buildings (i.e. a given W/H2 ratio), the mean leeward
375
and windward concentrations are constant whatever the H1/H2 ratio. However, the mean
376
concentration values are different, with concentrations globally twice as high on the leeward
377
side. This observation is linked to the characteristics of regime A described in section 4.1.
378
Indeed, for all the cases in which regime A occurs, a large clockwise rotating vortex appears
379
which spreads the pollutants released at ground level to the leeward side.
380
In regime B, the mean concentrations are no longer constant for a given distance between
381
buildings but depend on both ratios H1/H2 and W/H2. This time the mean concentrations are
382
higher on the windward side according to the counterclockwise vortex occurring in regime B,
383
which spreads the pollutants released at ground level to the windward side. The mean
384
concentrations on the windward side are globally three times higher than those of the leeward
385
side.
386
In regime C, the mean concentrations still depend on both ratios H1/H2 and W/H2 and the
387
concentrations are much higher than in regime B. The mean concentrations are globally higher
388
DOI : 10.1016/j.jweia.2019.104032
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on the leeward side but this is not always true. Indeed, for H1/H2=2.0 and W/H2=0.8, the mean
389
windward concentration is higher. It is much more difficult to interpret this difference than
390
those of the two previous regimes because two vortices are localized in the canyon in this case.
391
However, in this case the vortex is clockwise and localized near the emission source. The
392
pollutants released near the ground are thus initially spread to the leeward side and it is only
393
afterwards that the second vortex spreads them to the windward side. This explains why the
394
mean concentrations are mostly higher on the leeward side than on the windward side.
395
Finally, if we focus on how the mean concentrations evolve when the regimes change (e.g.
396
when switching from regime A to regime B), there is a notable difference between the windward
397
and leeward sides. Indeed, for a switch from regime A to regime B, whereas the mean
398
concentrations increase by a factor 6 on the windward side, the concentrations on the leeward
399
side are almost equal. Moreover, on the leeward side, the mean concentration observed in the
400
case of regime B did not increase much when H1/H2 increased or W/H2 decreased compared
401
to the windward side.
402
403
Fig. 9. Dimensionless windward profile averaged concentrations according to the ratios H1/H2 and W/H2.
404
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405
Fig. 10. Dimensionless leeward profile averaged concentrations according to the ratios H1/H2 and W/H2.
406
407
4.5.Mean concentration at ground level
408
Finally, the results were studied at ground level and Fig. 11 shows the dimensionless ground
409
averaged concentrations (i.e. the mean concentrations averaged over the ground profile)
410
proposed for several H1/H2 and W/H2 ratios; the different types of regime are also specified.
411
At ground level, the evolution of mean concentrations is similar for the leeward profile and the
412
whole street: regime A leads to constant mean concentrations for a given distance between
413
buildings; regime B leads to mean concentrations depending on both the distance between
414
buildings and difference in height between the two buildings; regime C leads to the same
415
observation as regime B, the difference being that for a given distance between buildings, a
416
maximal mean concentration is reached, after which this concentration decreases with the
417
increase in the difference in height between the two buildings.
418
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419
Fig. 11. Dimensionless ground profile averaged concentrations according to ratio H1/H2 and W/H2.
420
421
5. Discussion
422
Choices were made regarding the turbulence model used as well as the isothermal assumption
423
taken to fulfil this work. These choices could affect the presented results and are worth
424
discussing about.
425
Based on comparison with experimental data, the RNG turbulence model was selected. This
426
model is an isotropic linear k-ε based model that is known to have some limitations for highly
427
transient cases, especially in a wake of a body, including flows behind the leeward walls of
428
street canyons. To avoid such problems, non-linear turbulence models or anisotropic models
429
such as the Reynolds Stress Model (RSM) should be used. However, these models are time
430
consuming and are more difficult to converge. In addition, they seem to give not as much
431
improvements as expected in the case of isolated buildings or street canyons. Indeed,
432
Papageorgakis and Assanis (1999) showed that the linear RNG k-ε model gives significant
433
improvements compared to the standard model for recirculatory flow such for backward facing
434
step cases. Moreover, according to the same authors, the non-linear RNG model is not very
435
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attractive, yielding not to great improvements. Finally, Koutsouarakis et al. (2012) showed for
436
six street canyons with different aspect ratios that the RNG model gives the best performances
437
for each case compared to the standard model as well as compared to the RSM model.
438
The whole study was conducted considering neutral (isothermal) conditions since ambient and
439
wall temperatures were considered equal. Thus, only the forced convection due to the wind was
440
considered. More complex cases could appear when the building walls are heated by solar
441
radiations conducting to unstable conditions where natural convection appears. For this cases,
442
results in terms of recirculation regimes or pollutant concentrations can be different. Wang et
443
al. (2011) studied the cases of leeward, ground, and windward heated walls in a regular street
444
canyon and compared the results with the neutral case (without wall heating). They found that,
445
except for the case of the windward heated wall, the recirculation pattern in the street is always
446
the same. Concentrations are different depending on the case, but they are always lower than
447
for the neutral case. These results are confirmed by Allegrini et al. (2013) who did the same
448
work with several wind speed and also simulated a case where all walls are heated. This case
449
also leads to the same recirculation pattern as for the neutral case. According to these results, it
450
could be said that the results given in this study are not only good for one considering neutral
451
cases but are also a good first approximation of unstable cases. Pollutant concentrations being
452
greater for the neutral case than for the unstable case leading thus to a safer approach.
453
6. Conclusion
454
The effects of step-down street canyon geometric properties on recirculation patterns and mean
455
pollutant concentrations in a street were studied with a CFD model. This study considered 6
456
height ratios H1/H2 (from 1.0 to 2.0 with a 0.2 step) and 5 width ratios W/H2 (from 0.6 to 1.4
457
with a 0.2 step). The main conclusions are as follows:
458
(a) Three types of regimes can occur as a function of both the height and width ratios of the
459
street. Flow velocities and direction in the street, and thus pollutant concentrations,
460
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depend heavily on the type of regime being established. The three types of regime were
461
characterized by the number of vortices established and their direction: regime A
462
corresponded to a single clockwise vortex in the canyon; regime B corresponded to a
463
counter-clockwise vortex in the canyon and a clockwise vortex over the windward
464
building; regime C corresponded to two contra-rotating vortices in the canyon and a
465
clockwise vortex over the windward building.
466
(b) The critical values of H1/H2 corresponding to a change in the type of regime for a given
467
width ratio were determined. The critical values obtained were differed as a function of
468
the turbulence closure scheme used. These differences were never greater than 0.1 when
469
using standard or RNG k-epsilon turbulence schemes.
470
(c) Whatever the mean concentration considered (in the whole canyon, at pedestrian level
471
or near the building faces), the mean concentrations were lowest in the case of regime
472
A and highest in the case of regime C. Regime B therefore corresponded to an
473
intermediary state.
474
(d) The mean concentrations increased globally as differences in building height increased
475
(H1/H2 ratio), and with the decrease of street width (W/H2), except for the case of
476
regime A where the evolutions of mean concentrations depended only on street width.
477
(e) The quantitative evolution of the mean pollutant concentration in the whole street at
478
pedestrian level and near the building faces was proposed.
479
As a summary, in order to have a good ventilation in step-down street canyons and in the
480
perspective of reducing mean pollutant concentration of the whole street at pedestrian level and
481
near building faces, we recommend choosing carefully the height ratio H1/H2 as well as the
482
width ratio W/H2 in order to be in the case of a regime A.
483
These conclusions and results were obtained for a given type of street canyon and they should
484
be extended to consider other types such as step-up street canyons and wider and deeper
485
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canyons. Moreover, these results were obtained considering flat roofs. However, this type of
486
roof is not the only kind of roof used for buildings and further works should be carried out to
487
obtain information on other types of roof.
488
489
Acknowledgments
490
We would like to thank the ANRT (Association Nationale de la Recherche et de la Technologie)
491
for their support.
492
493
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494
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A: Fluid Dynamics 4, 15101520. https://doi.org/10.1063/1.858424
567
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... Hereinafter, they are known as the RANS-Eulerian and RANS-Lagrangian methods. The RANS-Eulerian method considers pollutants as a continuum by solving the passive scalar transport equation [14][15][16]. The RANS-Lagrangian method considers pollutants as discrete phases and tracks particle trajectories by solving particle dynamic equations [17][18][19][20]. ...
... First, the turbulent Schmidt number Sc t was considered to be 0.7, 0.5, 0.3, and 0.1, and adjusted in the SGDH based on the experimental values. The results based on Sc t = 0.3 were the closest to the experimental results in both fields, which is consistent with previous studies that predict pollutant dispersion in urban environments [14,15]. For simplicity, the results are presented in Appendix C. Because the D t limiter only affects the turbulent diffusivity of concentration near the source, a Sc t value that provides accurate predictions in the downwind should be adopted. ...
Article
To improve the prediction accuracy of pollutant dispersion near a source, a limiter for turbulent concentration diffusivity accounting for pollutant travel time is introduced into the Reynolds-averaged Navier–Stokes equations (RANS) and the Eulerian dispersion model. We predict pollutant dispersion in a two-dimensional street canyon and three-dimensional building arrays via the conventional modeling of turbulent diffusivity based on a simplified gradient diffusion hypothesis and the limiter. The results are validated using data from a wind tunnel experiment. Conventional modeling with Sct=0.3 yields results similar to experimental results. However, the mean concentration near the source is underestimated. The limiter reduces the turbulent diffusivity near the source by adjusting the combination of model parameters and improves the accuracy of concentration prediction. Based on the result with the limiter, we discovered that the turbulent Schmidt number near the source by the conventional definition can exceed three times that in the downwind region. Finally, we discuss the effect of the limiter on the net mass fluxes in pollutant removal. For the building arrays, the limiter increases the advective mass flux near the source and transports more pollutants toward the leeward wall, thereby decreasing the surface-sum spanwise turbulent-mass-flux at the source-located canyon side.
... At present, the main methods which are used to investigate the wind flow pattern and pollutant dispersion in street canyons are full-scale field measurements (Rotach et al. 2004;Niachou et al. 2008;Kwak et al. 2016), wind-tunnel (WT) experiments (Carpentieri and Robins 2010;Kastner-Klein and Plate 1999;Allegrini 2018), and computational fluid dynamics (CFD) simulations (Ahmadi et al. 2020;Huang et al. 2015;Reiminger et al. 2020). CFD simulations, under the premises of validation by the first two methods, are more and more applied to study the dispersion of traffic pollutants in street canyons because of their economical, comprehensive data and less constrained by external conditions. ...
... For example, as reviewed by Li et al. (2006), a number of researchers have examined the effects of the street width to building height ratio (AR) on in-canyon flow pattern and found out the critical ARs for flow regime transitions. Assimakopoulos et al. (2003), Xie et al. (2005), and Reiminger et al. (2020) performed CFD simulations to evaluate the wind flow and pollutant transport in step-down canyons (upwind building is taller than the downwind building) and step-up canyons (upwind building is shorter than the downwind building). They found that the asymmetric building configurations can induce obvious variations in flow structures and pollutant distributions. ...
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In this study, a validated CFD model is used to analyze the flow field and pollutant distribution in an isolated canyon (street aspect ratio, W/H = 1) by considering different street categories and arrangements of void deck under a perpendicular inflow wind. The results reveal that the street geometry affects significantly the in-canyon flow structures and thus the pollutant distributions. Comparing with the regular street canyon (a main clockwise vortex is obtained therein), the void deck can cause several vortices when a strong stream of air passes through the canyon. It is the most conducive to pollutant removal for the void decks at both buildings, while the construction with void deck at the upstream building causes pollutant accumulation on the windward side. Moreover, a larger high-pollution zone is generated above the elevated road due to the wind recirculation therein, and for the two-level street and the street with depressed road, the weak wind leads to the accumulation of traffic pollutants in the underground space. This study will provide technical support for urban street planning and design to alleviate traffic pollution.
... Fig. 1 illustrates the close relationship between air pollution and the in-canyon aerodynamic effects in case of AR > 0. 65. Due to the increasing concerns about urban air quality, the impact of street canyon morphology on in-canyon air quality gained more interest and numerous parameters, not only the impact of the AR [7,8], but also building height variations [9,10], roof shape [11,12] and the presence of trees [13,14] are examples that have been studied recently. ...
... [29] made mappings of the aspect ratios of different neighborhoods in the city of Antwerp but did not perform a city-wide analysis and did not correlate their results with air quality measurements. The study of [23] was performed as a preliminary study for our research paper, in which Nomenclature AR aspect ratio H var building height variance LAR lateral aspect ratio SVF sky view factor TV tot total daily traffic volume TV av average hourly traffic volume TV max maximum hourly traffic volume θ dev deviation from the main wind direction [5,58] sky view factor (SVF) no [24] building height variance yes [9,10] roof shape no [11,12] building permeability no K. [59,60]; presence of trees yes [13]; [75] Physical and chemical dynamics wall heating no [15,16] photochemical mechanisms no [18,21,22] a data available at street canyon level for the city of Antwerp. b selection of relevant recent publications. ...
Article
Air pollution remains a major environmental and health concern in urban environments, especially in street canyons that show increased pollution levels due to a lack of natural ventilation. Previous studies have investigated the relationship between street canyon morphology and in-canyon pollution levels. However, these studies are typically limited to the scale of a single street canyon and city-wide assessments on this matter are scarce. In 2018, NO2 concentrations were measured in 321 street canyons in the city of Antwerp (Belgium) as part of the large-scale citizen-science project “CurieuzeNeuzen”. In our research, this data was used to study the correlation between morphological indices (e.g. aspect ratio (AR), lateral aspect ratio (LAR), presence of trees) and the traffic volumes on a city-wide scale. The maximum hourly traffic volume (TVmax) and AR correlated significantly with the measured NO2 values, making them useful indicators for air quality in street canyons. For street canyons with AR > 0.65, a TVmax of 300 vehicles/hour was found as a threshold value to guarantee acceptable air quality. No significant correlations were found for the other parameters. Finally, a number of typical street canyon types were defined, which can be of fundamental interest for further research and spatial policy making.
... The backflow zone behind the building will increase as the side ratio decreases, while the vortex shedding will strengthen, resulting in lower pollutant concentrations [15]. In addition, Reiminger et al. [20] also emphasized that the pollutant diffusion between street canyons is greatly influenced by the ratio of building height and the ratio of street canyon width to windward building height. Compared with modifying the aspect ratio of street canyons, reasonable roof design can effectively remove pollutants around the building [21]. ...
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Poor wind environment in residential areas leads to the accumulation of odor from domestic waste, affecting pedestrian health. A reasonable arrangement of waste collection points can reduce pedestrian exposure risks. This study aims to investigate the hydrogen sulfide (H2S) dispersion and residents’ exposure risk at the pedestrian level for five different locations of waste collection points in a residential building array. Simulation results are consistent with the benchmark wind tunnel experiment, validating that the used turbulence model and numerical methods show good agreement with the predictions of the aforementioned problem. Results indicate that the dimensionless concentration of H2S and personal intake fraction in a residential area are lower when the collection point is at the corner of the building array periphery. When the collection point is located in the middle of the periphery of the building array or between two adjacent buildings in the center of the array, the local dimensionless concentration of H2S is 50 at the pedestrian level, and the personal intake fraction is three orders of magnitude higher than that at the corner of the building array periphery. The findings provide a reference for the layout of waste collection points in high-density residential areas and reduction in outdoor exposure risk.
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Air quality is a major health issue for densified cities nowadays. To evaluate and act upon it, modeling alongside sensors has proved to be a powerful tool. Among the different available models, Computational Fluid Dynamics (CFD) has proved to be formidable to evaluate airborne pollutant dispersion locally in urban areas since it is able to consider buildings and others complexes phenomenon at the scale of the meter. Nevertheless, this method has a major drawback, it is computationally expensive and cannot be applied in real time or over large areas. To overcome this issue, several state-of-the-art deep learning methods to treat spatial information have been trained based on CFD results to predict airborne pollutant dispersion. Among these models, multiResUnet architecture was proved to be the best on overall over seven metrics. It managed to have two out of three air quality metrics within satisfactory range for a good air quality model. These results are obtained in a mere matter of tens of seconds against several hours for CFD.
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Reducing the pollution concentration on the urban street ventilation is significant for creating a healthy urban living environment. In this study, the effects of source location on airflow patterns and pollutant transmission in street canyons under the influence of wind and thermal buoyancy forces have been investigated using the CFD approach. Also using the RNG k-ε model, RANS equations, and the Boussinesq model the structure of airflow, thermal behavior, and dispersion of pollutants in a deep street canyon in three-dimensional space have been evaluated. The results show that the combined effect of wind force and buoyancy effect enhances air circulation and turbulence of air layers in street canyons; the source location of pollution in a street canyon can significantly alter the distribution and concentration of pollution in the canyon. Sunward sides, as a thermal plume, transport pollutants vertically to the windward area; the impact of wind force also profoundly complicates transmission pathways of pollutants. Since the pollution sources in this study are the buildings around the street canyon, the findings of the present study can improve the understanding of patterns of pollution distribution in street canyons and be useful in developing design standards for high-rise buildings in dense urban areas.
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Based on the Reynolds-averaged Navier-Stokes (RANS) method, the standard k-ε turbulence model was used to simulate the effects of the side ratio (SR: building depth/building width, SR = 0.5, 1.0, 1.5, and 2.0, respectively) of isolated buildings on the flow field and pollutant of ethylene dispersion under unstable temperature stratification and neutral conditions. The results showed that under unstable temperature stratification, the updraft on the leeward side of the building caused the high temperature gas to flow upward and the temperature gradient to drop. Compared with the neutral condition, the length of the recirculation zone was shorter, and with the decrease in SR, the vortex in the recirculation zone became larger and more stable. Because the pollution source was located on the leeward side of the building, the pollutant gas was concentrated in the area between the pollution source and the leeward side of the building. The near-ground concentration of pollutants under unstable temperature stratification was lower than that under neutral conditions, and with the decrease in SR, the near-ground axis concentration decreased while the width of the pollutant plumes increased.
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The configuration of urban street-canyons, especially the ratio of the height of the buildings to the street width (H/W) and length to width (L/W), plays an essential role in directing and dispersion of wind flow and consequently affects changes in air temperature and urban heat islands (UHI). Despite many studies examining the aspect ratio of street-canyons, failure to follow these ratios from a specific order and organization and the lack of an optimal range for urban design is a gap seen in these studies. In addition, if the H/W exceeds a certain range, the results will change significantly and sometimes in reverse. Therefore, this paper simulates a residential town using CFD calculations in ANSYS-CFX 18. In two different scenarios (each scenario has four modes), wind flow and temperature changes were evaluated to find the optimal value of H/W and L/W. The analysis of changes in the three factors of wind velocity, temperature, and pressure show that the ratios H/W =1 and L/W=2 are the most suitable conditions for temperature reduction and UHI control. In addition, a sensitivity analysis confirms the generalizability of the obtained ratios to other fields with different temperature conditions and wind speeds.
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Computational fluid dynamics has shown a great interest among the scientific community to assess air pollutant concentrations in urban areas and, define strategies to limit air pollution and achieve sustainable cities of the future. Recent studies have given methodologies on how to assess mean annual concentrations based on numerical model results to compare with the annual air quality standards. Nonetheless, these methodologies need many wind directions to be modelled and, therefore, lead to high calculation costs. The purpose of this paper is to present two approaches to decrease the calculation costs when calculating annual concentration from computational fluid dynamics results by (1) ignoring uniformly spaced wind direction and (2) considering the predominant wind directions. According to the results, the first approach is on overall better than the second one for any wind rose or building layout considered. With the first approach, the calculation costs can be reduced up to 50% without leading to more than 20% of error, and even less error can be expected for homogeneous wind roses. Finally, a method to finely evaluate errors made when using the first approach versus using the whole wind rose, without computing it, is presented.
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As pollutions impose adverse effects on human health and environment, assessment of their dispersion within the urban regions can much help to control them. In urban regions, dynamics of pollutants will be affected by buildings and barriers, and to investigate the dispersion of the pollutants, these barriers must be considered. In this article, CFD simulation is done by applying the 3D approach, the k−ε Realizable turbulence model and two Schmidt numbers (0.3 and 0.7). It has seen that height, length and width of the building in front of the wind, and, the distance between the two buildings back to the main building (the building on which the stack is present), have much influence on the concentration of pollutions. Although there are some differences between the results with different Schmidt numbers, the trend of changes of the concentration in different locations is identical for the two Schmidt numbers.
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The transport and dispersion of pollutants in a street canyon, and the intersection between two streets, have been studied using wind-tunnel experiments and numerical simulations. The study of the street canyon demonstrates the importance of the geometry of the canyon (aspect ratio, asymmetry) in determining both the topology of the flow and the concentration distribution; the flow is also very sensitive to wind direction. The study of the street intersection shows how the intersection influences the flow and dispersion in the adjoining streets. This work has been used to develop new and practical models for flow and dispersion in city streets; these models are compared here with the results from wind-tunnel experiments and numerical simulations.
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This investigation was carried out to reveal the impact of solar radiation on wind flow structure and pollutant dispersion in an urban street canyon of aspect ratio of one using the computational fluid dynamic (CFD) technique. The simulation results (velocity and concentration data) show that heating from building wall surfaces and ground lead to strong buoyancy forces as the air is heated by the wall surface when receiving direct solar radiation. This thermally induced buoyancy plays a significant role in determining flow fields within street canyon. When the sun shines on the leeward side of the building and the ground, the airflow structure and pollutant dispersion patterns are similar to that without solar radiation, the buoyancy flux adds to the upward advection flux along the wall strengthening the original vortex. When the windward wall is warmer than the air, an upward buoyancy flux opposes the downward advection flux along the wall, and divides the flow structure into two counter-rotating vortices indicating a clockwise top vortex and a reverse lower vortex within the canyon. Further, the impact of various temperature differences on the windward heating and different velocities for inlet velocity has been examined. The relative influence of the thermal effect can be estimated by bulk Richardson number (R b).
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In this paper the flow over regular arrangements of buildings with slanted roofs is numerically studied and its impact on pollutant dispersion is analyzed. By systematically varying the roof slope, we could identify the switching point between a one- and a two-vortex regime inside the street canyons between the buildings. In the one-vortex regime, the pollutant concentration in the street canyon is found to decrease with increasing roof slope, which is related to the rotational speed of the canyon vortex and the aerodynamic roughness felt by the fully-developed flow aloft the street canyons. In the two-vortex regime limited mixing occurs between both vortex cores, resulting in higher near-ground pollutant concentrations. Compared to the widely studied flat-roof case, slightly upward slanted roofs exhibit a lower aerodynamic roughness, yet yield similar air quality in the street canyon.